IBM pastry chef
Watson, the data-crunching super computer from IBM, may be leaving its part-time occupation as the world's most feared Jeopardy contestant in favor of more rewarding opportunities — namely those in medical science, consulting and even baking. "That’s not something we thought of when we started with Watson," admits John Kelly III, senior research VP for IBM, on the topic of Watson in the kitchen.
Although Watson won't be fancifully tossing pizza dough or shoveling finely sliced chiffonade into a pot of simmering stock any time soon, IBM's impressive piece of hardware does seem fairly adept at combining ingredients — be they for innovative food recipes or pharmaceuticals.
For Chef James Briscione, Waston has been assisting in creating innovative, tasty recipes. The thinking-machine bases its recipes on thousands of existing recipes, the "olfactory pleasantness" of certain ingredients and other factors.
In one example, Watson was asked to create an unusual but "healthy" breakfast pastry. The computer generated a Spanish crescent pastry with cocoa, saffron, pepper, almonds and honey. However, Watson made the decision to substitute butter — a ubiquitous ingredient for pastries — with vegetable oil in an effort to make it “healthier.”
I suppose even Watson knows there isn't much left you can do to make a pastry “healthy.” Even so, the pasty was “pretty good,” according to the IBM director who had the pleasure of eating it.
After Watson digested all available information on Malaria and existing drugs used to combat the illness, the super computer not only identified known anti-malaria drugs but also spit out an additional 15 combinations of chemicals which may achieve the same effect.
"It doesn't just answer questions, it encourages you to think more widely," pharmaceutical company VP Catherine Peishoff said, suggesting Watson's ability to extrapolate conclusions from enormous data sets could help researchers stumble onto things they would have missed otherwise. "It essentially says, ‘Look over here, think about this.’ That’s one of the exciting things about this technology."
Meanwhile, Watson has been helping one mining company streamline its operations in Australia. The company, Thiess, has an enormous $3 billion fleet of large machines and trucks used for its projects. After analyzing data from hundreds of equipment-mounted sensors, weather, weights, speeds, terrain and more, Watson utilized predictive analysis to generate suggestions for improving operational efficiency. Thiess has been implementing the suggestions and the results will be monitored over a six-month period.